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Low-dimensional morphospace of topological motifs in human fMRI brain networks

Sarah E. Morgan, Sophie Achard, Maite Termenon, Edward T. Bullmore, Petra E. Vértes
2018 Network Neuroscience  
We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs.  ...  The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs and we observe that two principal components (PCs) can account for 97% of the motif  ...  In particular, we consider randomised networks which do not preserve the degree distribution, randomised networks which do preserve the degree distribution and randomised networks in which the wavelet  ... 
doi:10.1162/netn_a_00038 pmid:30215036 pmcid:PMC6130546 fatcat:2nf2c5tlzzfnzjaaatlumf733i

Multiplex lexical networks reveal patterns in early word acquisition in children

Massimo Stella, Nicole M. Beckage, Markus Brede
2017 Scientific Reports  
In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected  ...  according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity.  ...  We also confirmed that the improvement in performance of optimised multiplex parameters is strongly dependent on the structure and overlap of layers, as experiments on randomised multiplex networks result  ... 
doi:10.1038/srep46730 pmid:28436476 pmcid:PMC5402256 fatcat:qbdqwwj5wbbvzg5tu7htwy2wza

Subgraph ensembles and motif discovery using an alternative heuristic for graph isomorphism

Kim Baskerville, Maya Paczuski
2006 Physical Review E  
The network's structure is described using statistical properties of its N-node subgraphs for N≤ 14.  ...  The method is applied to sample subgraphs from an E.coli protein interaction network, and as a probe for discovery of extended motifs.  ...  P. thanks Lee Smolin and the Perimeter Institute for their hospitality during the initiation of this research, and Stefan Boettcher for conversations about Zipf plots and motif discovery.  ... 
doi:10.1103/physreve.74.051903 pmid:17279935 fatcat:ydx6nhzytjasrpwyzpl562y4sq

Large-Scale Automatic Species Identification [chapter]

Jeff Mo, Eibe Frank, Varvara Vetrova
2017 Lecture Notes in Computer Science  
We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset.  ...  multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising  ...  Deep convolutional networks have also been considered for species classification.  ... 
doi:10.1007/978-3-319-63004-5_24 fatcat:tpwfixaknrdlhf6byfkubm62jq

The Identification of Similarities between Biological Networks: Application to the Metabolome and Interactome

Adrian P. Cootes, Stephen H. Muggleton, Michael J.E. Sternberg
2007 Journal of Molecular Biology  
Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps.  ...  The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process  ...  in a pair of randomised networks.  ... 
doi:10.1016/j.jmb.2007.03.013 pmid:17466331 fatcat:ules2temgba3bilohz24yj5jvi

Agriculture fleet vehicle routing: A decentralised and dynamic problem

Marin Lujak, Elizabeth Sklar, Frederic Semet
2020 AI Communications  
We focus on the dynamic and decentralised version of this problem applicable in environments involving multiple agriculture machinery and farm owners where concepts of fairness and equity must be considered  ...  This is especially the case when considering overall fleet performance, its efficiency and scalability in the context of highly automated agriculture vehicles that perform tasks throughout multiple fields  ...  AGRI-FLEETS" project ANR-20-CE10-0001 funded by the French National Research Agency (ANR) and by the UK Research and Innovation (UKRI) Research England council's "Lincoln Agri-Robotics" project as part of  ... 
doi:10.3233/aic-201581 fatcat:smvtysz2q5a4vn3ab3irfsznk4

Low dimensional morphospace of topological motifs in human fMRI brain networks [article]

Sarah E. Morgan, Sophie Achard, Maite Termenon, Edward T. Bullmore, Petra E. Vértes
2017 bioRxiv   pre-print
There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network.  ...  We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs.  ...  Since we only consider 4-node motifs, we drop the superscript 4 for brevity.  ... 
doi:10.1101/153320 fatcat:4zxefzvbwjd45fyet7wprfsnga

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

Gorka Zamora-López, Yuhan Chen, Gustavo Deco, Morten L. Kringelbach, Changsong Zhou
2016 Scientific Reports  
These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs.  ...  networks.  ...  Acknowledgements The authors thank Mario Senden for discussions and early revision of the manuscript. This work has been supported by (G.  ... 
doi:10.1038/srep38424 pmid:27917958 pmcid:PMC5137167 fatcat:mgv3v5s36jc4lgbpjcun6tjal4

A Machine Learning Approach for Detecting Unemployment using the Smart Metering Infrastructure

Casimiro A. Curbelo Montanez, William Hurst
2020 IEEE Access  
distributed Internet of Things (IoT) sensor network.  ...  with dropout, closely followed by the results produced by a distance weighted discrimination with polynomial kernel model.  ...  The strength of RF derives from using random subsamples of the training data (bootstrap aggregation or bagging) and randomising the algorithm for learning cased-level classifiers.  ... 
doi:10.1109/access.2020.2969468 fatcat:cmt5jzszqzdbpl2yy7dx4msg7u

Rich-Cores in Networks

Athen Ma, Raúl J. Mondragón, Bin Jiang
2015 PLoS ONE  
A core is said to be a group of central and densely connected nodes which governs the overall behavior of a network.  ...  Interestingly, the definition of a rich-club naturally emphasizes high degree nodes and divides a network into two subgroups.  ...  Consider w min is the minimal weight linking two nodes in a network and the link between nodes i and j has a weight of w ij .  ... 
doi:10.1371/journal.pone.0119678 pmid:25799585 pmcid:PMC4370710 fatcat:ed2haf5devhfxjtafilwnf44hy

A multi-swarm optimisation approach for spam detection in online social networks

R. Krithiga, E. Ilavarasan
2021 International Journal of Vehicle Information and Communication Systems  
Online Social Networks (OSNs) play a crucial role in communication systems for rapid message broadcasting and information sharing.  ...  Hence, in this paper, a novel Multi-Swarm-Whale Optimisation Algorithm (MS-WOA) is proposed for feature selection to detect spam profiles on Facebook.  ...  Social networks play a crucial role in communication systems for rapid message broadcasting and information sharing. These are actively used to share information within a network of users.  ... 
doi:10.1504/ijvics.2021.113495 fatcat:w433uh3a3vbrjbfavidi6ul474

Preserving physically important variables in optimal event selections: a case study in Higgs physics

Philipp Windischhofer, Miha Zgubič, Daniela Bortoletto
2020 Journal of High Energy Physics  
Analyses of collider data, often assisted by modern Machine Learning methods, condense a number of observables into a few powerful discriminants for the separation of the targeted signal process from the  ...  We present a novel method based on a differentiable estimate of mutual information, a measure of non-linear dependency between variables, to construct a discriminant that is statistically independent of  ...  Acknowledgments The authors are grateful to Gregor Kasieczka and David Shih for help with DisCo and for pointing out a problem with the implementation of the original decorrelation approach.  ... 
doi:10.1007/jhep07(2020)001 fatcat:ttk3325l4vhwheypplu3boi7wq

DESIGN ISSUES ASSOCIATED WITH NEURAL NETWORK SYSTEMS APPLIED WITHIN THE ELECTRONICS MANUFACTURING DOMAIN

A. A. WEST, C. J. HINDE, C. H. MESSOM, R. HARRISON, D. J. WILLIAMS
2000 Journal of electronics manufacturing  
provides optimised convergence performance.  ...  A comparison of the designed solution with standard approaches to neural network implementation is given.  ...  A basic understanding of the required network topology to produce appropri-ate discrimination (e.g. threshold and banding) of the process parameters has been shown to result in an optimised network from  ... 
doi:10.1142/s0960313100000046 fatcat:2l4nwto3djfszghptt3ffh3k7m

A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

Zena M. Hira, Duncan F. Gillies
2015 Advances in Bioinformatics  
Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources.  ...  A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide.  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.  ... 
doi:10.1155/2015/198363 pmid:26170834 pmcid:PMC4480804 fatcat:mnlyfcwcarhqjix63e46x63nka

TMVA - Toolkit for Multivariate Data Analysis [article]

A. Hoecker, P. Speckmayer, J. Stelzer, J. Therhaag, E. von Toerne, H. Voss, M. Backes, T. Carli, O. Cohen, A. Christov, D. Dannheim, K. Danielowski (+14 others)
2009 arXiv   pre-print
Integrated into the analysis framework ROOT, TMVA is a toolkit which hosts a large variety of multivariate classification algorithms.  ...  Training, testing, performance evaluation and application of all available classifiers is carried out simultaneously via user-friendly interfaces.  ...  Acknowledgements The fast growth of TMVA would not have been possible without the contribution and feedback from many developers (also co-authors of this Users Guide) and users to whom we are indebted.  ... 
arXiv:physics/0703039v5 fatcat:xec4mqsl5rgsrnljp2z2n6brj4
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